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An object-coding genetic algorithm for integrated process planning and scheduling

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  • Zhang, Luping
  • Wong, T.N.

Abstract

Process planning and jobshop scheduling problems are both crucial functions in manufacturing. In reality, dynamic disruptions such as machine breakdown or rush order will affect the feasibility and optimality of the sequentially-generated process plans and machining schedules. With the approach of integrated process planning and scheduling (IPPS), the actual process plan and the schedule are determined dynamically in accordance with the order details and the status of the manufacturing system. In this paper, an object-coding genetic algorithm (OCGA) is proposed to resolve the IPPS problems in a jobshop type of flexible manufacturing systems. An effective object-coding representation and its corresponding genetic operations are suggested, where real objects like machining operations are directly used to represent genes. Based on the object-coding representation, customized methods are proposed to fulfill the genetic operations. An unusual selection and a replacement strategy are integrated systematically for the population evolution, aiming to achieve near-optimal solutions through gradually improving the overall quality of the population, instead of exploring neighborhoods of good individuals. Experiments show that the proposed genetic algorithm can generate outstanding outcomes for complex IPPS instances.

Suggested Citation

  • Zhang, Luping & Wong, T.N., 2015. "An object-coding genetic algorithm for integrated process planning and scheduling," European Journal of Operational Research, Elsevier, vol. 244(2), pages 434-444.
    • Handle: RePEc:eee:ejores:v:244:y:2015:i:2:p:434-444
    DOI: 10.1016/j.ejor.2015.01.032
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    References listed on IDEAS

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    1. Goncalves, Jose Fernando & de Magalhaes Mendes, Jorge Jose & Resende, Mauricio G. C., 2005. "A hybrid genetic algorithm for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 167(1), pages 77-95, November.
    2. Li, Xinyu & Shao, Xinyu & Gao, Liang & Qian, Weirong, 2010. "An effective hybrid algorithm for integrated process planning and scheduling," International Journal of Production Economics, Elsevier, vol. 126(2), pages 289-298, August.
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    Citations

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    Cited by:

    1. Liangliang Jin & Qiuhua Tang & Chaoyong Zhang & Xinyu Shao & Guangdong Tian, 2016. "More MILP models for integrated process planning and scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4387-4402, July.
    2. Zhang, Haowei & Xie, Junwei & Ge, Jiaang & Zhang, Zhaojian & Zong, Binfeng, 2019. "A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar," European Journal of Operational Research, Elsevier, vol. 272(3), pages 868-878.
    3. Xu Zhang & Hua Zhang & Jin Yao, 2020. "Multi-Objective Optimization of Integrated Process Planning and Scheduling Considering Energy Savings," Energies, MDPI, vol. 13(23), pages 1-31, November.
    4. Zhu, Xuedong & Son, Junbo & Zhang, Xi & Wu, Jianguo, 2023. "Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem," Omega, Elsevier, vol. 117(C).
    5. Hyun Cheol Lee & Chunghun Ha, 2019. "Sustainable Integrated Process Planning and Scheduling Optimization Using a Genetic Algorithm with an Integrated Chromosome Representation," Sustainability, MDPI, vol. 11(2), pages 1-23, January.
    6. Alexander Hübner & Fabian Schäfer & Kai N. Schaal, 2020. "Maximizing Profit via Assortment and Shelf‐Space Optimization for Two‐Dimensional Shelves," Production and Operations Management, Production and Operations Management Society, vol. 29(3), pages 547-570, March.
    7. Zhang, Sicheng & Li, Xiang & Zhang, Bowen & Wang, Shouyang, 2020. "Multi-objective optimisation in flexible assembly job shop scheduling using a distributed ant colony system," European Journal of Operational Research, Elsevier, vol. 283(2), pages 441-460.
    8. Yongkai An & Wenxi Lu & Weiguo Cheng, 2015. "Surrogate Model Application to the Identification of Optimal Groundwater Exploitation Scheme Based on Regression Kriging Method—A Case Study of Western Jilin Province," IJERPH, MDPI, vol. 12(8), pages 1-22, July.
    9. Jin Huang & Liangliang Jin & Chaoyong Zhang, 2017. "Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission," Sustainability, MDPI, vol. 9(10), pages 1-18, September.
    10. Barzanji, Ramin & Naderi, Bahman & Begen, Mehmet A., 2020. "Decomposition algorithms for the integrated process planning and scheduling problem," Omega, Elsevier, vol. 93(C).

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